Learning of Molecular Graphs in Toxicity Prediction

In-silico toxicity prediction plays a key role in the health industry and research. Machine learning has been widely rewarded for its high efficiency in this field, but the methods are mostly expertise driven and with limited growth. After mitigating many practical problems smartly, deep learning has also been entrusted with high expectations in toxicity prediction. In this work, we attempted to predict compound toxicity by leveraging a graph representation of the compounds and a neat graph-learning framework. Key atomic and bond features were captured by the graph representations, and different graph-learning techniques in the framework were investigated. As evaluated on the Tox21 data, the graph-learning framework possesses a good potential in attaining state-of-the-art performances and handling imbalanced data in toxicity prediction tasks.